Gene_clustering

library(factoextra)

Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

library(COTAN)
library(ggrepel)
library(Rtsne)
library(plotly)

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library(tidyverse)

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library(htmlwidgets)
library(MASS)

Attaching package: ‘MASS’

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library(dendextend)

---------------------
Welcome to dendextend version 1.14.0
Type citation('dendextend') for how to cite the package.

Type browseVignettes(package = 'dendextend') for the package vignette.
The github page is: https://github.com/talgalili/dendextend/

Suggestions and bug-reports can be submitted at: https://github.com/talgalili/dendextend/issues
Or contact: <tal.galili@gmail.com>

    To suppress this message use:  suppressPackageStartupMessages(library(dendextend))
---------------------


Attaching package: ‘dendextend’

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To demostrate how to do the gene clustering usign COTAN we begin importing the COTAN object that stores all elaborated data and, in this case, regarding a mouse embrionic cortex dataset (developmental stage E17.5).

input_dir = "Data/"
layers = list("L1"=c("Reln","Lhx5"), "L2/3"=c("Satb2","Cux1"), "L4"=c("Rorb","Sox5") , "L5/6"=c("Bcl11b","Fezf2") , "Prog"=c("Vim","Hes1"))
#objE17 = readRDS(file = paste(input_dir,"E17.5_cortex.cotan.RDS", sep = ""))
objE17 = readRDS(file = paste(input_dir,"E17.5_cortex.cotan.RDS", sep = ""))

g.space = get.gene.coexpression.space(objE17, 
                                      n.genes.for.marker = 28,#25  
                                      primary.markers = unlist(layers))

[1] "calculating gene coexpression space: output tanh of reduced coex matrix"
     L11      L12    L2/31    L2/32      L41      L42    L5/61    L5/62    Prog1    Prog2 
  "Reln"   "Lhx5"  "Satb2"   "Cux1"   "Rorb"   "Sox5" "Bcl11b"  "Fezf2"    "Vim"   "Hes1" 
[1] "Get p-values on a set of genes on columns genome wide on rows"
[1] "Using function S"
[1] "function to generate S "
[1] "function to generate S "

g.space = as.data.frame(as.matrix(g.space))

coex.pca.genes <- prcomp(t(g.space),
                 center = TRUE,
                 scale. = F) 

fviz_eig(coex.pca.genes, addlabels=TRUE,ncp = 10)

#fviz_eig(coex.pca.genes, choice = "eigenvalue", addlabels=TRUE)

Hierarchical clustering

# clustering usign Ward method
hc.norm = hclust(dist(g.space), method = "ward.D2")

# and cut the tree into 5 clusters (for example)
cut = cutree(hc.norm, k = 7, order_clusters_as_data = F)

# It crates the tree
dend <- as.dendrogram(hc.norm)

# I can use a dataframe from the pca to store some data regarding the clustering
pca_1 = as.data.frame(coex.pca.genes$rotation[,1:5])
pca_1 = pca_1[order.dendrogram(dend),]


mycolours <- c("genes related to L5/6" = "#3C5488FF","genes related to L2/3"="#F39B7FFF","genes related to Prog"="#4DBBD5FF","genes related to L1"="#E64B35FF","genes related to L4" = "#91D1C2FF", "not marked"="#B09C85FF")

# save the cluster number into the dataframe
pca_1$hclust = cut

dend =branches_color(dend,k=7,col=c("#4DBBD5FF","#91D1C2FF","#F39B7FFF","#E64B35FF","#3C5488FF","#91D1C2FF","#B09C85FF" ),groupLabels = T)
#dend =color_labels(dend,k=5,labels = rownames(pca_1),col=pca_1$colors)



# plot the dendrogram with all anmes for secondary markers
dend %>%
  set("labels", ifelse(labels(dend) %in% rownames(pca_1)[rownames(pca_1) %in% colnames(as.matrix(g.space))], labels(dend), "")) %>%
  #  set("branches_k_color", value = c("gray80","#4DBBD5FF","#91D1C2FF" ,"gray80","#F39B7FFF","#E64B35FF","#3C5488FF"), k = 7) %>%
 plot(horiz=F, axes=T)#,ylim = c(0,80))

or just with primary markers

dend %>%
set("labels", ifelse(labels(dend) %in% rownames(pca_1)[rownames(pca_1) %in% unlist(layers)], labels(dend), "")) %>%
  set("branches_k_color", value = c("gray80","#4DBBD5FF","#91D1C2FF" ,"gray80","#F39B7FFF","#E64B35FF","#3C5488FF"), k = 7) %>%
plot(horiz=F, axes=T,ylim = c(0,100))

Now we can plot the PCA

# some more genes as landmarks
controls =list("genes related to L5/6"=c("Foxp2","Tbr1"), "genes related to L2/3"=c("Mef2c"), "genes related to Prog"=c("Nes","Sox2") , "genes related to L1"=c() , "genes related to L4"=c()) 

# dataframe to be able to label only primary markers and control genes
textdf <- pca_1[rownames(pca_1) %in% c(unlist(layers),unlist(controls)) , ]

 for (m in c(1:length(controls))) {
  for (g in controls[[m]]) {
    if(g %in% rownames(textdf)){
      textdf[g,"highlight"] = names(controls[m])
    } 
  }
}

# deciding the colors
mycolours <- c("genes related to L5/6" = "#3C5488FF","genes related to L2/3"="#F39B7FFF","genes related to Prog"="#4DBBD5FF","genes related to L1"="#E64B35FF","genes related to L4" = "#91D1C2FF", "not marked"="#B09C85FF")

# to assing correcly the cluster number and the color
mycolours2 = c("Reln","Satb2","Rorb","Bcl11b","Vim")
names(mycolours2) = unique(cut[unlist(layers)])

mycolours2[mycolours2 == "Reln"] = "#E64B35FF"
mycolours2[mycolours2 == "Satb2"] = "#F39B7FFF"
mycolours2[mycolours2 == "Rorb"] = "#91D1C2FF"
mycolours2[mycolours2 == "Bcl11b"] = "#3C5488FF"
mycolours2[mycolours2 == "Vim"] = "#4DBBD5FF"
color_to_add = unique(pca_1$hclust)[!unique(pca_1$hclust) %in% as.numeric(names(mycolours2))]
names(color_to_add) = unique(pca_1$hclust)[!unique(pca_1$hclust) %in% as.numeric(names(mycolours2))]
color_to_add[color_to_add %in% 
                 unique(pca_1$hclust)[!unique(pca_1$hclust) %in% as.numeric(names(mycolours2))]] = "#B09C85FF"
mycolours2 = c(mycolours2,color_to_add)

pca1 = ggplot(subset(pca_1,!hclust %in% unique(cut[unlist(layers)])  ), aes(x=PC1, y=PC2)) +  geom_point(alpha = 0.3,color = "#B09C85FF",size=1)

#pca2 = pca1 + geom_point(data = subset(pca_1, highlight != "not marked" ), aes(x=PC1, y=PC2, colour=hclust),size=2.5,alpha = 0.9) 
pca2 = pca1 + geom_point(data = subset(pca_1, hclust %in% unique(cut[unlist(layers)]) ), aes(x=PC1, y=PC2, colour=as.character(hclust)),size=1,alpha = 0.5) + 
 scale_color_manual( "Status", values = mycolours2)  +
  scale_fill_manual( "Status", values = mycolours2)  +
  xlab("") + ylab("") +
  geom_label_repel(data =textdf , aes(x = PC1, y = PC2, label = rownames(textdf),fill=as.character(hclust)),
                   label.size = NA, 
                   alpha = 0.5, 
                   direction = "both",
                   na.rm=TRUE,
                   seed = 1234) +
  geom_label_repel(data =textdf , aes(x = PC1, y = PC2, label = rownames(textdf)),
                   label.size = NA, 
                   segment.color = 'black',
                   segment.size = 0.5,
                   direction = "both",
                   alpha = 1, 
                   na.rm=TRUE,
                   fill = NA,
                   seed = 1234) +
  ggtitle("PCA") +
  theme_light(base_size=10) +
    theme(axis.text.x=element_blank(),plot.title = element_text(size=14, 
                                    face="italic", 
                                    color="#3C5488FF",
                                    hjust=0.01,
                                    lineheight=1.2,margin = margin(t = 5, b = -15)),
        axis.text.y=element_blank(),
        legend.position = "none")  # titl)

pca2 #+ geom_encircle(data = pca_1, aes(group=hclust)) 

t-SNE code and plot

# run the t-SNE
cl.genes.tsne = Rtsne(g.space ,initial_dims = 100, dims = 2, perplexity=30,eta = 200, verbose=F, max_iter = 3000,theta=0.4,num_threads = 10,pca_center = T, pca_scale = FALSE, normalize = T )

d_tsne_1 = as.data.frame(cl.genes.tsne$Y)
rownames(d_tsne_1) = rownames(g.space)

d_tsne_1 = d_tsne_1[order.dendrogram(dend),]

# save the cluster numebr inside a dataframe with the t-SNE information
d_tsne_1$hclust = cut

d_tsne_1$names = rownames(d_tsne_1)

# as before to label only some genes
textdf <- d_tsne_1[rownames(d_tsne_1) %in% c(unlist(layers),unlist(controls)),]

for (m in c(1:length(controls))) {
  for (g in controls[[m]]) {
    if(g %in% rownames(textdf)){
      textdf[g,"highlight"] = names(controls[m])
    } 
  }
}


 p1 = ggplot(subset(d_tsne_1,!hclust %in% unique(cut[unlist(layers)])), aes(x=V1, y=V2)) +  geom_point(alpha = 0.3, color = "#B09C85FF", size=1)

p2 = p1 + geom_point(data = subset(d_tsne_1, hclust %in% unique(cut[unlist(layers)]) ), aes(x=V1, y=V2, colour=as.character(hclust)),size=1,alpha = 0.5) +
    scale_color_manual("Status", values = mycolours2)  +
  scale_fill_manual("Status", values = mycolours2)  +
  xlab("") + ylab("")+
  geom_label_repel(data =textdf , aes(x = V1, y = V2, label = names,fill=as.character(hclust)),
                   label.size = NA, 
                   alpha = 0.5, 
                   direction = "both",
                   na.rm=TRUE,
                   seed = 1234) +
  geom_label_repel(data =textdf , aes(x = V1, y = V2, label = names),
                   label.size = NA, 
                   segment.color = 'black',
                   segment.size = 0.5,
                   direction = "both",
                   alpha = 1, 
                   na.rm=TRUE,
                   fill = NA,
                   seed = 1234) +
  ggtitle("t-SNE") +
  theme_light(base_size=10) +
  theme(axis.text.x=element_blank(),plot.title = element_text(size=14, 
                                    face="italic", 
                                    color="#3C5488FF",
                                    hjust=0.01,
                                    lineheight=1.2,margin = margin(t = 5, b = -15)),
        axis.text.y=element_blank(),
        legend.position = "none")  # titl)
p2

Code to create an iteractive plot. This can be modified to be used with all the plots.

p = ggplot(d_tsne_1, aes(x=V1, y=V2, text= paste("gene: ",names))) +  
  geom_point(size=2, aes(colour=as.character(hclust)), alpha=0.8) +
  scale_color_manual("Status", values = mycolours2)  +
  xlab("") + ylab("") +
  ggtitle("t-SNE") +
  theme_light(base_size=10) +
  theme(axis.text.x=element_blank(),
        axis.text.y=element_blank())

ggplotly(p)

Multidimensional scaling (MDS) and plot

# run the MDS
genes.dist.euc =  dist(g.space, method =  "euclidean")
#fit <- isoMDS(genes.dist.euc) # not linear
fit <- isoMDS(genes.dist.euc)

initial  value 12.149889 
final  value 10.248596 
converged

fit.genes = as.data.frame(fit$points)

fit.genes = fit.genes[order.dendrogram(dend),]

fit.genes$hclust = cut


fit.genes$names = rownames(fit.genes)

mycolours3 <- c("cluster L5/6 markers" = "#3C5488FF","cluster L2/3 markers"="#F39B7FFF","cluster Prog markers"="#4DBBD5FF","cluster L1 markers"="#E64B35FF","cluster L4 markers" = "#91D1C2FF", "not identified cluster"="#B09C85FF")

#fit.genes$hclust = factor(cutree(hc.norm, 7))
used = vector()
for (k in c(1:length(layers))) {
  #print(k)
  tt =as.numeric(cut[layers[[k]]][1])
  fit.genes[fit.genes$hclust == tt,"cluster"] = paste("cluster",names(layers[k]),"markers", sep = " " )
  used = c(used,cut[layers[[k]]][1])
}

fit.genes[fit.genes$hclust %in% (unique(fit.genes$hclust)[!unique(fit.genes$hclust) %in% used]),]$cluster = "not identified cluster"

textdf <- fit.genes[rownames(fit.genes) %in% c(unlist(layers),unlist(controls)),]

   f1 = ggplot(subset(fit.genes,!hclust %in% unique(cut[unlist(layers)]) ), aes(x=V1, y=V2)) +  geom_point(alpha = 0.3, color = "#B09C85FF", size=1)

f2 = f1 + geom_point(data = subset(fit.genes, hclust %in% unique(cut[unlist(layers)]) ), 
                     aes(x=V1, y=V2, colour=cluster), size=1,alpha = 0.5) +
  scale_color_manual("Status", values = mycolours3)  +
  scale_fill_manual("Status", values = mycolours3)  + 
  xlab("") + ylab("")+
  geom_label_repel(data =textdf , aes(x = V1, y = V2, label = rownames(textdf),fill=cluster),
                   label.size = NA, 
                   alpha = 0.5, 
                   direction ="both",
                   na.rm=TRUE,
                   seed = 1234, show.legend = FALSE) +
  geom_label_repel(data =textdf , aes(x = V1, y = V2, label = rownames(textdf)),
                   label.size = NA, 
                   segment.color = 'black',
                   segment.size = 0.5,
                   direction = "both",
                   alpha = 1, 
                   na.rm=TRUE,
                   fill = NA,
                   seed = 1234, show.legend = FALSE) +
  ggtitle("MDS") +
  theme_light(base_size=10) +
  theme(axis.text.x=element_blank(),plot.title = element_text(size=14, 
                                    face="italic", 
                                    color="#3C5488FF",
                                    hjust=0.01,
                                    lineheight=1.2,margin = margin(t = 5, b = -15)),
        axis.text.y=element_blank(),
        legend.title = element_blank(),
        legend.text = element_text(color = "#3C5488FF",face ="italic" ),
        legend.position = "bottom")  # titl)


f2 #+ geom_encircle(data = fit.genes, aes(group=`5_clusters`)) 

sessionInfo()

R version 4.0.4 (2021-02-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] dendextend_1.14.0 MASS_7.3-53.1     htmlwidgets_1.5.3 forcats_0.5.1     stringr_1.4.0     dplyr_1.0.4      
 [7] purrr_0.3.4       readr_1.4.0       tidyr_1.1.2       tibble_3.0.6      tidyverse_1.3.0   plotly_4.9.3     
[13] Rtsne_0.15        factoextra_1.0.7  ggrepel_0.9.1     ggplot2_3.3.3     Matrix_1.3-2      data.table_1.13.6
[19] COTAN_0.1.0      

loaded via a namespace (and not attached):
  [1] readxl_1.3.1         backports_1.2.1      circlize_0.4.12      plyr_1.8.6           igraph_1.2.6        
  [6] lazyeval_0.2.2       splines_4.0.4        crosstalk_1.1.1      listenv_0.8.0        scattermore_0.7     
 [11] digest_0.6.27        htmltools_0.5.1.1    viridis_0.5.1        magrittr_2.0.1       tensor_1.5          
 [16] cluster_2.1.1        ROCR_1.0-11          openxlsx_4.2.3       ComplexHeatmap_2.6.2 globals_0.14.0      
 [21] modelr_0.1.8         matrixStats_0.58.0   colorspace_2.0-0     rvest_0.3.6          rappdirs_0.3.3      
 [26] haven_2.3.1          xfun_0.20            crayon_1.4.0         jsonlite_1.7.2       spatstat_1.64-1     
 [31] spatstat.data_2.0-0  survival_3.2-7       zoo_1.8-8            glue_1.4.2           polyclip_1.10-0     
 [36] gtable_0.3.0         leiden_0.3.7         GetoptLong_1.0.5     car_3.0-10           future.apply_1.7.0  
 [41] shape_1.4.5          BiocGenerics_0.36.0  abind_1.4-5          scales_1.1.1         DBI_1.1.1           
 [46] rstatix_0.7.0        miniUI_0.1.1.1       Rcpp_1.0.6           viridisLite_0.3.0    xtable_1.8-4        
 [51] clue_0.3-58          reticulate_1.18      foreign_0.8-81       latex2exp_0.4.0      stats4_4.0.4        
 [56] httr_1.4.2           RColorBrewer_1.1-2   ellipsis_0.3.1       Seurat_4.0.0         ica_1.0-2           
 [61] pkgconfig_2.0.3      farver_2.0.3         sass_0.3.1           uwot_0.1.10          dbplyr_2.1.0        
 [66] deldir_0.2-10        tidyselect_1.1.0     labeling_0.4.2       rlang_0.4.10         reshape2_1.4.4      
 [71] later_1.1.0.1        cellranger_1.1.0     munsell_0.5.0        tools_4.0.4          cli_2.3.0           
 [76] generics_0.1.0       broom_0.7.5          ggridges_0.5.3       evaluate_0.14        fastmap_1.1.0       
 [81] yaml_2.2.1           goftest_1.2-2        fs_1.5.0             knitr_1.31           fitdistrplus_1.1-3  
 [86] zip_2.1.1            RANN_2.6.1           pbapply_1.4-3        future_1.21.0        nlme_3.1-152        
 [91] mime_0.9             xml2_1.3.2           rstudioapi_0.13      compiler_4.0.4       curl_4.3            
 [96] filelock_1.0.2       png_0.1-7            ggsignif_0.6.1       spatstat.utils_2.0-0 reprex_1.0.0        
[101] bslib_0.2.4          stringi_1.5.3        basilisk.utils_1.2.2 lattice_0.20-41      vctrs_0.3.6         
[106] pillar_1.4.7         lifecycle_0.2.0      lmtest_0.9-38        jquerylib_0.1.3      GlobalOptions_0.1.2 
[111] RcppAnnoy_0.0.18     cowplot_1.1.1        irlba_2.3.3          httpuv_1.5.5         patchwork_1.1.1     
[116] R6_2.5.0             promises_1.1.1       rio_0.5.16           KernSmooth_2.23-18   gridExtra_2.3       
[121] IRanges_2.24.1       parallelly_1.23.0    codetools_0.2-18     assertthat_0.2.1     rjson_0.2.20        
[126] withr_2.4.1          SeuratObject_4.0.0   sctransform_0.3.2    S4Vectors_0.28.1     mgcv_1.8-33         
[131] parallel_4.0.4       hms_1.0.0            grid_4.0.4           rpart_4.1-15         basilisk_1.2.1      
[136] rmarkdown_2.7        carData_3.0-4        Cairo_1.5-12.2       ggpubr_0.4.0         shiny_1.6.0         
[141] lubridate_1.7.9.2